Signal processing in hearing aids is usually implemented by determining a time-varying gain for a signal, and then multiplying the signal within by the gain. This approach gives a linear time-varying system, that is, a filter with a frequency response that changes over time. This system can be very effective for those types of processing, such as dynamic-range compression and noise suppression, where the desired signal processing is a time- and frequency-dependent gain. But because of its linear nature, a time-varying filter cannot be used to implement nonlinear processing such as frequency shifting or phase randomization as disclosed by Applicant in the subject application.
An alternative approach is to use an analysis/synthesis system. For the analysis the incoming signal is usually divided into segments, and each segment is analyzed to determine a set of signal properties. For the synthesis, a new signal is generated using the measured or modified signal properties. An effective analysis/synthesis procedure is sinusoidal modeling known from U.S. Pat. No. 4,885,790, U.S. RE 36,478 and U.S. Pat. No. 4,856,068. In sinusoidal modeling the speech is divided into overlapping segments. The analysis consists of computing a fast Fourier transform (FFT) for each segment, and then determining the frequency, amplitude, and phase of each peak of the FFT. For the synthesis, a set of sinusoids is generated. Each sinusoid is matched to a peak of the FFT; not all peaks are necessarily used. Rules are provided to link the amplitude, phase, and frequency of a peak in one segment to the corresponding peak in the next segment, and the amplitude, phase, and frequency of each sinusoid is interpolated across the output segments to give a smoothly varying signal. The speech is thus reproduced using a limited number of modulated sinusoidal components.
Sinusoidal modeling provides a framework for nonlinear signal modifications. The approach can be used, for example, for digital speech coding as shown in U.S. Pat. No. 5,054,072. The amplitudes and phases of the signal are determined for the speech, digitally encoded, and then transmitted to the receiver where they are used to synthesize sinusoids to produce the output signal.
Sinusoidal modeling is also effective for signal time-scale and frequency modifications as reported in McAulay, R. J., and Quatieri, T. F. (1986), “Speech analysis/synthesis based on a sinusoidal representation”, IEEE Trans. Acoust. Speech and Signal Processing, Vol ASSP-34, pp 744-754. For time-scale modification, the frequencies of the FFT peaks are preserved, but the spacing between successive segments of the output signal can be reduced to speed up the signal or increased to slow it down. For frequency shifting the spacing of the output signal segments is preserved along with the amplitude information for each sinusoid, but the sinusoids are generated at frequencies that have been shifted relative to the original values. Another signal manipulation is to reduce the peak-to-average ratio by dynamically adjusting the phases of the synthesized sinusoids to reduce the signal peak amplitude as shown in U.S. Pat. No. 4,885,790 and U.S. Pat. No. 5,054,072.
Sinusoidal modeling can also be used for speech enhancement. In Quatieri, T. F, and Danisewicz, R. G. (1990), “An approach to co-channel talker interference suppression using a sinusoidal model for speech”, IEEE Trans Acoust Speech and Sginal Processing, Vol 38, pp 56-69 sinusoidal modeling is used to suppress an interfering voice, and Kates (reported in Kates, J. M. (1994), “Speech enhancement based on a sinusoidal model”, J. Speech Hear Res, Vol. 37, pp 449-464) has also used sinusoidal modeling as a basis for noise suppression. In the above mentioned Kates study, the high-intensity sinusoidal components of the signal assumed to be speech were reproduced but low-intensity components assumed to be noise were removed; however, no benefit in improving speech intelligibility was found. Jensen and Hansen (reported in Jensen, J., and Hansen, J. H. L. (2001), “Speech enhancement using a constrained iterative sinusoidal model”, IEEE Trans Speech and Audio Proc, Vol 9, pp 731-740) used sinusoidal modeling to enhance speech degraded by additive broadband noise, and found their approach to be more effective than the comparison schemes such as Wiener filtering.
Sinusoidal modeling has also been applied to hearing loss and hearing aids. Rutledge and Clements (reported in U.S. Pat. No. 5,274,711) used sinusoidal modeling as the processing framework for dynamic-range compression. They reproduced the entire signal bandwidth using sinusoidal modeling, but increased the amplitudes of the synthesized components at those frequencies where hearing loss was observed. A similar approach has been used by others to provide frequency lowering for hearing-impaired listeners by shifting the frequencies of the synthesized sinusoidal components lower relative to those of the original signal. The amount of shift was frequency-dependent, with low frequencies receiving a small amount of shift and higher frequencies receiving an increasingly larger shift.